Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. This study investigates multi-omics lung cancer data obtained from The Cancer Genome Atlas (TCGA) repository. Differentially expressed genes were identified using four statistical approaches: LIMMA, T-test, Canonical Correlation Analysis (CCA), and the Wilcoxon test applied across gene expression (GE), DNA methylation, and microRNA (miRNA) datasets. Kernel Machine Regression (KMR) was subsequently employed to perform data fusion across the multi-modal datasets. The empirical results highlight notable interactions among GE, miRNA expression, and DNA methylation in lung cancer. Our analysis identified 38 genes that show significant associations with lung cancer. Among these, 8 genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, and ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that may offer promising therapeutic potential against lung cancer. The effectiveness of these candidate drugs is further reinforced by evidence from independent research studies, which emphasize their potential in lung cancer treatment.

Programming is an important skill for advancement in computer science and information and communication technology (ICT). Online judge (OJ) systems are increasingly popular platforms in programming education to enhance users programming skills through practice. However, in most OJ systems, the problems are usually listed in a simple format without clear identification by difficulty levels. This research aims to cluster problem difficulty effectively and analyze user performance. We extract features from the Aizu OJ (AOJ) submission log data and estimate user ability and problem difficulty scores using logistic regression (LR). We propose an approach that combines an attention mechanism (AM) with K-means applied to statistically selected features via pairwise correlation to categorize the problems. Compared to standard K-means and Fuzzy C-means (FCM) with their AM integration on full and reduced feature sets by principal component analysis (PCA), autoencoder (AE), and variance inflation factor (VIF), the proposed method demonstrates the best clustering performance. It achieves the highest silhouette score (SS) of 0.507, Calinski-Harabasz index (CHI) of 6581.088, and a comparatively good Davis Bouldin index (DBI) of 0.625.We found a strong correlation between user ability and problem-solving rates; beginners and intermediate users consistently struggle with solving more complex problems. To support this, web-based clustering and group-based recommendations have been developed that visualize problem difficulty clusters, enabling users to understand difficulty levels and select problems based on their ability level. Moreover, the proposed approach is promising for effective clustering of more complex educational datasets and improving learning outcomes in programming education.

Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils that assign models to Architect, Skeptic, Secretary, Pedagogue, and Mentor roles and operate in both Blind and Guided modes. Single-model tutors achieve near-perfect repair on course problems but perform less reliably on challenge cases and often rewrite large portions of student code, show non-negligible false positive rates, and leak full or near-full solutions in a substantial share of hints. Councils designed around measured model strengths improve both technical and pedagogical behaviour. On the challenge track, the best council raises patch success by 12.2 percentage points over the best single tutor, while reducing false positives, shrinking median patch size, improving hint localisation, and cutting solution leakage in Blind mode from about one fifth of hints to under ten percent. Councils also exhibit higher stability across reruns and produce hints that two independent instructors consistently rate as more useful and better scaffolded. Guided mode, where internal components see a reference solution, yields further technical gains but introduces leakage risks that require prompt tightening and a sanitising Secretary to control the flow of ground truth. Additional trap experiments with poisoned reference solutions show a mix of resistance and fail-safe collapse rather than systematic poisoning of hints. These results indicate that orchestration and information flow are powerful levers and that well-designed councils can provide more reliable and pedagogically aligned debugging support than strong single-model tutors alone.

Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils that assign models to Architect, Skeptic, Secretary, Pedagogue, and Mentor roles and operate in both Blind and Guided modes. Single-model tutors achieve near-perfect repair on course problems but perform less reliably on challenge cases and often rewrite large portions of student code, show non-negligible false positive rates, and leak full or near-full solutions in a substantial share of hints. Councils designed around measured model strengths improve both technical and pedagogical behaviour. On the challenge track, the best council raises patch success by 12.2 percentage points over the best single tutor, while reducing false positives, shrinking median patch size, improving hint localisation, and cutting solution leakage in Blind mode from about one fifth of hints to under ten percent. Councils also exhibit higher stability across reruns and produce hints that two independent instructors consistently rate as more useful and better scaffolded. Guided mode, where internal components see a reference solution, yields further technical gains but introduces leakage risks that require prompt tightening and a sanitising Secretary to control the flow of ground truth. Additional trap experiments with poisoned reference solutions show a mix of resistance and fail-safe collapse rather than systematic poisoning of hints. These results indicate that orchestration and information flow are powerful levers and that well-designed councils can provide more reliable and pedagogically aligned debugging support than strong single-model tutors alone.